The digital transformation of enterprises has changed from internal business, organization and business model to digital synergy of production factors and organizational relationship in the upper and lower reaches of supply chain. Based on the data-to-information-to-knowledge-to-wisdom model (DIKW) of data value chain, this paper constructs a multi-stage model of enterprise digital transformation, and uses the data of Chinese A-share listed companies from 2007 to 2021, this paper examines the impact of digital transformation on supply chain resilience. The study found that digital transformation can significantly improve supply chain resilience, with different effects at different stages. In mechanism testing, digital transformation promotes supply chain resilience by promoting information and knowledge spillover in supply chain. Further heterogeneity analysis found that when the enterprise is located in the eastern region, the economic policy uncertainty is high and the industry competition degree is strong, as well as the large-scale, high-tech industry and non-manufacturing industry, for enterprises with low supply chain integration, the impact of digital transformation on supply chain resilience is more significant. Moreover, the digital transformation has the diffusion effect on the upstream and downstream enterprises of the supply chain, which can improve the digital transformation degree and value creation level of the upstream and downstream enterprises. The research results reveal the impact and mechanism of enterprise digital transformation on the resilience of supply chain, provide a new idea for building a resilient supply chain system, and promoting coordinated development of supply chain.

Considering the high volatility and complexity of electricity spot price time series, a combined forecasting model based on wavelet transform and LGBM (light gradient boosting machine, LGBM) is proposed. By introducing rolling time window and wavelet transform, the dynamic multi-scale decomposition of electricity spot price series can be realized, and the frequency characteristics can be extracted to reduce its modal complexity and effectively avoid data leakage. In this study, the proposed model is constructed by utilizing the complex nonlinear feature extraction ability of the LGBM algorithm. The spot market data of Shanxi electric power is used to verify the validity of the proposed model. The results show that the proposed model is superior to the mainstream forecasting methods such as long-term and short-term memory model, support vector machine, elastic network regression model and extreme gradient lifting model in many key performance indexes, such as root mean square error, average absolute error and determination coefficient, among which the $ R^2 $ reaches 0.9792, showing high forecasting accuracy. At the same time, the proposed model shows robustness and adaptability under different market conditions, which shows the proposed model can be seen as a reliable forecasting tool for power market participants and helps to optimize trading strategies and reduce market risks.

This study approaches typhoon landfalls as exogenous climate risk events, designating the moment of landfall as the critical intervention point. Utilizing the difference-in-differences (DID) methodology, the research examines the influence of typhoon disasters on the stock returns of publicly traded companies in China, and assesses how financial risks propagate through supply chain networks triggered by typhoon disasters. To gain a more nuanced understanding of these effects, the paper engages in a detailed mechanism analysis by examining the intensity of digital transformation. The results suggest that typhoon disasters have a significant and detrimental impact on the stock returns of firms located in affected areas, with this effect rippling through to their suppliers and customers via the intricate web of supply chain connections. Moreover, the study uncovers a distinct asymmetry in the spillover effects between suppliers and customers. Specifically, the research highlights that the level of digital transformation is instrumental in alleviating the financial risks associated with typhoons and serves as a protective barrier against the adverse effects on stock returns. Finally, a comprehensive suite of robustness checks reinforces the validity and reliability of the study’s conclusions.

This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple large language models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs’economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: With explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a multi-LLM-agent-based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs’ human-like reasoning capabilities and computational power.

The fundamental methodologies of machine learning and econometrics are reviewed. We also discuss the challenges of integrating the data-driven and model-based causal approaches and conjecture how it may yield new insights to empirical economic studies.

How to measure the climate transition risk faced by the high-carbon industry, as well as how to effectively identify and mitigate the systemic risk spillover and contagious effects of the high-carbon industry, is an important issue facing policymakers and the academic community. This paper constructs a high-dimensional time-varying vector autoregression index model is used to measure the spillover effects within and between high-carbon industries. The research finds that the high-carbon industry faces the highest climate transition risk, while the financial industry has the lowest. The greater the climate transition risk, the higher the systemic risk faced by listed companies, and the stranded assets of the high-carbon industry are an important transmission path. Under different policy backgrounds, the risk spillover effects of the high-carbon industry to related industries show differentiated inclinations, and the main risk spillover targets of the high-carbon industry are all associated with their own production or financial networks. The banking industry always performs as a risk absorption role at the center of the risk network and is highly associated with the high-carbon industry. This paper provides a basis for governments and regulatory authorities to understand the impact of transition risk on the high-carbon industry and the industry correlation, and provides certain reference value for resolving the cross-industry transmission of systemic risks in the high-carbon industry.

As the scope, actors, forms, approaches, trends, and influencing factors of innovation inputs continue to evolve, the research objects, domains, and methodological perspectives of innovation inputs analysis are continuously expanding and becoming more refined. The optimal allocation, efficient management, and strategic decision-making of innovation inputs necessitate a systematic and scientific measurement framework. This study develops a theoretical framework for the innovametrics of innovation inputs, emphasizing their role throughout the innovation process. The framework aims to provide analytical perspectives and methodological tools for addressing key measurement issues related to the level, structure, and influencing factors of innovation inputs. Based on a review of the evolution of research on innovation input measurement, this study categorizes key measurement issues into three dimensions: development, structure, and dynamics. It further proposes the key issues and analytical approaches associated with each dimension. Additionally, considering advancements in innovation input management and practical demands, this study outlines future research directions in innovametrics. The development of this theoretical framework not only advances the theoretical and methodological foundations for optimizing innovation input allocation, management, and decision-making but also provides a systematic framework to guide academia, policymakers, and industry practitioners in understanding and effectively applying relevant measurement theories and methodologies.

With the increasing demand for rural delivery in mountainous areas, the routing problem of rural delivery logistics in mountainous areas (RPRDLMA) has become an academic hotspot. Based on the background of rural passenger, cargo and postal integration development, the RPRDLMA under the cooperative distribution of bus-electric vehicle-drone (RPRDLMA-CDBEVD) is studied in this paper. Firstly, the village service points are divided into type TC and type FC, meaning that they are served by EVs or by drones, according to their geographic location, distribution characteristics and volume of cargo delivered or mailed. Next, a continuous function of bus idle capacity is established based on the tidal rural passenger flow characteristics. Then, the RPRDLMA-CDBEVD model is constructed with the goal of total cost minimization. Specifically, the total cost includes commissioning cost, capacitybased cost, distance-based cost, time-based cost and electricity consumption cost. In order to solve the model, a hybrid algorithm of multi-constraint modified clustering algorithm and improved adaptive genetic algorithm (MCDCA-IAGA) is designed. The experimental results and case studies show that the collaborative delivery mode of passenger shuttle bus electric vehicle unmanned aerial vehicle effectively reduces delivery costs by 2.9% and delivery time by 8.6%, providing a feasible solution for logistics path planning in mountainous and rural areas.

Individual selection preferences and the abuse of recommendation algorithms have trapped the public in an information cocoon dilemma. It would trigger differentiated collective behavior, exacerbate the formation of opinion polarization, and even have a serious impact on social public order. In this paper, we systematically analyze the effects of differences in public behavior within the information cocoon on the interaction between heterogeneous opinion groups, including the intra-group homogeneity restriction weakening-strengthening effect and the inter-group inhibition-promotion combination interaction effect. Then, based on the Lotka-Volterra modeling approach, the opinion polarization dynamic model with the interaction of heterogeneous opinions is constructed. Besides, the equilibrium points and their stabilities are estimated, too. Moreover, we also explore the law of opinion polarization through numerical simulations and empirical analysis. The results show that under the influence of the information cocoon, the weaker the intra-group homogeneity restriction and the stronger the inter-group promotion effect, the faster and the larger the expansion of the two groups, and the more likely to generate binary polarization situation. What's more, when the inter-group inhibition effect is stronger and the intra-group homogeneous restriction of heterogeneous opinion is weaker, the expansion rate of the group would slow down and the size would decrease and even disappear after reaching the peak, and generate single polarization situation. In addition, the potential diffusion range positively affects the expansion rate and final size of the group itself. Furthermore, the potential diffusion range would also slow down the expansion of the heterogeneous group under the inter-group promotion effect, but does not affect its final size.

Carbon neutrality is of great significance to the sustainable development of human society, and carbon neutrality technology and ecological carbon sequestration are two important factors affecting carbon neutrality capacity. In this paper, we develop an economic growth model that takes into account both factors, while also considering the deadline for carbon neutrality. By the theory of optimal control, we obtain closed-form formulas for optimal consumption, investment, capital stock, and carbon neutrality capacity. Based on theoretical and numerical analysis, several policy recommendations are proposed. Specifically, countries need to set carbon-neutral targets that match their own endowments and target capital stocks. Countries or regions within the same country should choose different technical levels of carbon-neutral investment according to their different stages. Unlike usual expectations, the path of carbon neutralization capacity may decrease with the elasticity of output to investment. As the deadline approaches, investment strategies may be abnormal.

Climate change has emerged as a new source of instability in the global financial system, making the scientific identification and assessment of its transmission channels to the financial sector a critical issue in the field of climate finance. Currently, climate-related financial risk modeling and practical applications still face numerous obstacles. In this context, this paper reviews several key developments in climate-related financial risk studies, including the characteristics of climate risks in financial markets, the methodologies and practices for assessing climate financial risks, and future research directions. To be specific, this study first elaborates on three crucial features of climate financial risks. Second, it systematically reviews three streams of approaches for climate financial risk assessment developed in recent years, analyzes their applicability and limitations, and examines relevant practices adopted by central banks and financial regulators across different countries. Finally, the paper identifies promising directions for future research to support both theoretical advancement and practical implementation in the field of climate financial risk assessment.

As an innovation and development of Marxist productive forces theory, new quality productive forces have received widespread attention since its proposal. In accordance with the connotation and characteristics of new quality productive forces, this study constructs an indicator system based on TEI@I methodology. With the comprehensive consideration of industrial development status and government’s catalytic role, we collect basic data from four aspects: Industry, workers, infrastructure, and policy basis, and explore relevant policy texts to measure new quality productive forces of 31 provinces in China. The results show that the development level of China’s new quality productive forces is at the early stage, and has a certain space aggregation and uneven development between the east and the west. The construction of new infrastructure and the cultivation of new workers will be the key points to the development of new quality productive forces in the future.

In this article, we use text analysis to extract implicit information such as specialization of economic governance from government work reports, providing a new explanation for the sources of deviation in local economic growth goals. The results are that the specialization of economic governance can bring economic growth exceeding expectations, which is reflected in the fact that the actual economic growth rate exceeds the expected goals announced in the government work report. This is related to the effective allocation of resource elements, and is also motivated by factors such as “promotion championships”. With the transformation of local government performance evaluation system, the impact of economic governance specialization on the deviation of economic growth goals has decreased. However, in cities with different regions or administrative levels, professional officials are effective in promoting economic growth. Furthermore, if there are too many prospects for the future, weak execution ability, and lower innovation as well as higher compliance with previous policies in the local government’s economic governance, that may reduce the impact of specialization in economic governance on the deviation of economic growth targets, which is not conducive to achieving economic growth exceeding expectations. This study has reference significance for better leveraging the role of the government in resource allocation as well as in economic growth.

The rise of large language models has injected fresh vigor into the development of Robo-advisors in China and promoted the innovation of financial technology. In this context, this paper constructs an AI agent based on the domestic generative language model framework, from sentiment analysis, market prediction, factor indicators and other dimensions, in-depth mining of alternative data and traditional financial data in stock trading signals, and then constructs the Chinese stock market investment and trading strategies. Empirical studies show that AI agent based on the domestic large models have the potential to perform quantitative analysis, and that the investment returns under the combined multiple dimensions will be significantly better than the results of a single dimension. This suggests that by utilizing powerful natural language processing capabilities and data analysis capabilities, the application of domestic large models in Robo-advisors is promising, providing new ideas and methods for the continuous development of quantitative investment. With the evolution of technology, the future AI agent will be able to understand market dynamics and investor needs more deeply, thus providing more targeted support for investment decisions, enhancing overall investment returns and creating more value.

In time series forecasting, prediction error metrics cannot assist researchers in determining whether poor prediction performance is due to an inappropriate model choice or if the data inherently lacks predictive information. Intrinsic predictability characterizes "the upper limit of prediction accuracy" for the data, which can help researchers assess the compatibility of the current model and data. In this paper, we briefly review the concepts of predictability and provide a detailed introduction to the studies of time series predictability based on permutation entropy. Based on this, we propose permutation entropy with covariates to characterize the complexity of target time series when covariates are available and demonstrate its effectiveness through experiments with real glass bubble data. Additionally, we further present a strategy for model selection based on intrinsic predictability, aiming to choose simpler models and reduce the time cost of modeling and forecasting while maintaining reasonable accuracy. Numerical experiments on economic data validate the efficacy of this strategy.

In response to the scenario of a two-stage production structure that includes undesirable outputs and shared input factors, a two-stage data envelopment analysis (DEA) model has been developed. This model not only enables the rational allocation of shared resources between the two stages but also addresses undesirable outputs by applying the weak disposability theory, which aligns with real-world production dynamics. Furthermore, drawing on the concept of non-cooperative games, the model decomposes the efficiency of subprocesses by considering scenarios in which either the first or second stage is dominant, thereby establishing subprocess efficiency models. Ultimately, we employ the proposed model to evaluate the innovation efficiency of Specialized, Refined, Distinctive, and Innovative (SRDI) small and medium-sized enterprises in Fujian Province. By conducting a thorough analysis of both the overall efficiency and the subprocess efficiency of these enterprises, more accurate and comprehensive evaluation results can be obtained. Additionally, comparisons with various models further enhance the rationale and feasibility of the model presented in this paper.

This paper examines the impact of digital inclusive finance on the operational performance of regional commercial banks using a fixed-effects model based on balanced panel data from 78 urban and rural commercial banks spanning from 2011 to 2021. The results indicate a significant negative relationship between the two. This conclusion remains valid after addressing endogeneity issues and conducting robustness tests, suggesting that the current competitive crowding-out effect still exerts a substantial influence. Further analysis through moderation and threshold effects reveal that the technology spillover effect of digital inclusive finance drives business innovation and enhances risk-taking capacity among regional commercial banks, thereby mitigating their negative effects, with the moderation effect on risk-taking being more pronounced. The threshold parameter estimates show that business innovation has a more significant negative convergence moderation effect on rural commercial banks, while risk-taking exhibits a more significant negative convergence moderation effect on urban commercial banks. The findings of this study provide important practical insights for the digital transformation of regional commercial banks and the sustainable and healthy development of regional economies.

This paper preliminarily explores the potential application of the automated market maker (AMM) mechanism in China’s Central Bank Digital Currency (CBDC) for cross-border transactions. The study focuses on two primary approaches to managing exchange rate volatility risks through AMM: one relies on direct regulation via central bank reserves, while the other achieves reserve-free regulation through market incentives. The research indicates that the reserve-free regulation mechanism, by setting target ranges and providing economic incentives, can effectively guide liquidity concentration and reduce exchange rate volatility risks, while simultaneously decreasing reliance on foreign exchange reserves. However, this mechanism may also exert certain influences on market liquidity distribution and price discovery. Future research could further investigate dynamic market behaviors, policy coordination, and international cooperation to refine the exchange rate regulation mechanisms in CBDC cross-border transactions, manage risks associated with the cross-border use of the digital yuan, and provide theoretical support and practical references for the internationalization of the digital yuan.

The environment of international politics and economics is becoming increasingly complex and ever-changing, posing great challenges to the resilience and security of industry chains and supply chains. As an important part in supply chain management, procuring decisions are now influenced by various uncertain factors (such as supply disruption, transportation disruption, price volatility), thus directly affecting the cost of enterprises and the resilience of supply chains. This paper provides a review of the resilient supplier selection and order allocation problem, providing a basic description and a general framework for the problem. Especially, this paper focuses on different aspects (such as the four different types of risk and the corresponding modeling, the risk response strategies, the three mainstream mathematical modeling methods, commonly considered factors, and the solving algorithms etc) to review the problem. Finally, this paper states insights into future research trends.

Exploring the risk spillover effects between energy and metal futures has important practical significance for improving the quality and efficiency of systemic risk supervision and ensuring the smooth operation of commodity futures markets. However, most existing studies are limited to the low-order moment level and fail to fully reveal the cross-market risk transmission mechanism. Given this, this paper integrates the autoregressive conditional density model and the time-varying parameter vector autoregressive extended joint connectedness approach to explore the higher-order moment and cross-moment risk spillover effects between China’s energy and metal futures markets; secondly, the nonparametric causality-in-quantile test is used to study the Granger causality relationship between geopolitical risks and total spillovers. Empirical results show that risk spillovers between energy and metal markets show significant differences under different moments, and the total spillover of high-order moments is lower than the total volatility spillover. Copper dominates the volatility and skewness spillover, while fuel oil dominates the kurtosis spillover. Copper skewness, zinc kurtosis, and fuel oil skewness dominate the three cross-moment spillover effects, respectively. The dynamic total spillover and net spillover indexes show significant time-varying characteristics and have risen sharply after the outbreak of major crises such as the COVID-19 epidemic and the Russia-Ukraine war. Geopolitical risk is a key driver of energy-metal spillovers, and its predictive power on cross-moment total spillovers is significantly higher than conditional volatility and higher-order moment total spillovers.